Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169745
Paul Akash Gunturu, Rony Joseph, Emany Sri Revant, S. Khapre
Investing in the stock market is an essential aspect of the financial sector. However, the task of identifying lucrative stocks is a challenging one that requires careful analysis. This study aims to address this challenge by comparing various Machine Learning and Deep Learning techniques for predicting stock trends. The research evaluates and compares different models, including Long Short-Term Memory (LSTM), Prophet (Automated Forecasting Procedure), Random Decision Forest, Auto-ARIMA, k-Nearest Neighbors (KNN), Linear Regression, and Moving Average techniques like SMA and EMA. Furthermore, a new hybrid model is proposed, which outperforms existing models in terms of accuracy. The models are trained and tested on a historical dataset of stocks from different industrial sectors and evaluated based on various performance metrics. The study provides insights into the accuracy of different prediction models and can help investors, traders, and financial analysts make informed investment decisions. Additionally, the findings of this research work can serve as a benchmark for future research on stock market prediction.
{"title":"Survey of Stock Market Price Prediction Trends using Machine Learning Techniques","authors":"Paul Akash Gunturu, Rony Joseph, Emany Sri Revant, S. Khapre","doi":"10.1109/ICAIA57370.2023.10169745","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169745","url":null,"abstract":"Investing in the stock market is an essential aspect of the financial sector. However, the task of identifying lucrative stocks is a challenging one that requires careful analysis. This study aims to address this challenge by comparing various Machine Learning and Deep Learning techniques for predicting stock trends. The research evaluates and compares different models, including Long Short-Term Memory (LSTM), Prophet (Automated Forecasting Procedure), Random Decision Forest, Auto-ARIMA, k-Nearest Neighbors (KNN), Linear Regression, and Moving Average techniques like SMA and EMA. Furthermore, a new hybrid model is proposed, which outperforms existing models in terms of accuracy. The models are trained and tested on a historical dataset of stocks from different industrial sectors and evaluated based on various performance metrics. The study provides insights into the accuracy of different prediction models and can help investors, traders, and financial analysts make informed investment decisions. Additionally, the findings of this research work can serve as a benchmark for future research on stock market prediction.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125388084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169633
Neha Gahlan, Divyashikha Sethia
Electroencephalography (EEG) is useful for mapping emotions directly from the brain, but its heterogeneous signals make it challenging to extract features accurately. Prior works for emotion classification uses EEG data without removing data heterogeneity leading to misclassification or inaccurate classification. This paper proposes an EMD-based methodology for EEG data that segments signals into multiple IMFs to remove heterogeneity and extract significant features. The proposed approach uses a Feed-Forward Neural Network (FFNN) to classify emotions via the VAD model and shows a 5-6% increment in accuracy, precision, and recall scores for emotion classification. Experimental results demonstrate good evaluation performance scores for classifying emotional states on two publicly accessible emotional datasets, AMIGOS and DREAMER.
{"title":"Three Dimensional Emotion State Classification based on EEG via Empirical Mode Decomposition","authors":"Neha Gahlan, Divyashikha Sethia","doi":"10.1109/ICAIA57370.2023.10169633","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169633","url":null,"abstract":"Electroencephalography (EEG) is useful for mapping emotions directly from the brain, but its heterogeneous signals make it challenging to extract features accurately. Prior works for emotion classification uses EEG data without removing data heterogeneity leading to misclassification or inaccurate classification. This paper proposes an EMD-based methodology for EEG data that segments signals into multiple IMFs to remove heterogeneity and extract significant features. The proposed approach uses a Feed-Forward Neural Network (FFNN) to classify emotions via the VAD model and shows a 5-6% increment in accuracy, precision, and recall scores for emotion classification. Experimental results demonstrate good evaluation performance scores for classifying emotional states on two publicly accessible emotional datasets, AMIGOS and DREAMER.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116523818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169201
Archana Saini, Kalpna Guleria, Shagun Sharma
With the rapid growth of internet users, spam emails have become a major problem. Spammers can easily create fake profiles and email accounts by pretending to be genuine people in the sent emails. The spammers target people who are unaware of such scams. In today’s environment, email is a simple, quick, and cost-effective way to communicate but has various security threats which are necessary to identify to maintain security. This situation necessitates having an inbuilt spam filtering system to use email effectively without being worried about losing personal details. The goal of this work is to discover and predict spam emails early by using various classifiers. Machine learning methods provide the most accurate spam classification. This article contributes towards the development of a spam detection model by using multiple classification methods to tackle spam email challenges and helps in the technological progress in privacy & security. This model employs classification technologies such as naive bayes, K*, J48, and random forest. Conclusively, when the random forest model has been used as a prediction classifier, the output of this model has shown the highest accuracy of 95.48%.
{"title":"Machine Learning Approaches for an Automatic Email Spam Detection","authors":"Archana Saini, Kalpna Guleria, Shagun Sharma","doi":"10.1109/ICAIA57370.2023.10169201","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169201","url":null,"abstract":"With the rapid growth of internet users, spam emails have become a major problem. Spammers can easily create fake profiles and email accounts by pretending to be genuine people in the sent emails. The spammers target people who are unaware of such scams. In today’s environment, email is a simple, quick, and cost-effective way to communicate but has various security threats which are necessary to identify to maintain security. This situation necessitates having an inbuilt spam filtering system to use email effectively without being worried about losing personal details. The goal of this work is to discover and predict spam emails early by using various classifiers. Machine learning methods provide the most accurate spam classification. This article contributes towards the development of a spam detection model by using multiple classification methods to tackle spam email challenges and helps in the technological progress in privacy & security. This model employs classification technologies such as naive bayes, K*, J48, and random forest. Conclusively, when the random forest model has been used as a prediction classifier, the output of this model has shown the highest accuracy of 95.48%.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129877680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169125
Somil Gambhir, Sanya Khanna, Priyanka Malhotra
Lumpy skin disease is a transmissible virus contracted by cattle that has led to concern among the nations. It has a direct relation with climate as the latter plays a major role in studying the infection and the pattern of transmission followed by it. This study depicts how the various climatic factors help in determining whether the cattle in the specific region or a country has the lumpy skin disease or not by using machine learning algorithms. Machine learning algorithms employed in the present study predicted lumpy disease with accuracy and F1 score of 100% and 1.0, respectively. In the present study, four different machine learning algorithms: Adaboost, K-nearest neighbors, decision tree and random forest are employed. The present research suggests that the decision trees can be used to predict lumpy skin disease infection using the geospatial and climatic parameters. The predicting power of machine learning algorithms can help in monitoring the disease spread patterns. It will also help in the application of vaccine campaigns in regions where the spread of disease poses a great risk to health.
{"title":"Machine Learning Based Diagnosis of Lumpy Skin Disease","authors":"Somil Gambhir, Sanya Khanna, Priyanka Malhotra","doi":"10.1109/ICAIA57370.2023.10169125","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169125","url":null,"abstract":"Lumpy skin disease is a transmissible virus contracted by cattle that has led to concern among the nations. It has a direct relation with climate as the latter plays a major role in studying the infection and the pattern of transmission followed by it. This study depicts how the various climatic factors help in determining whether the cattle in the specific region or a country has the lumpy skin disease or not by using machine learning algorithms. Machine learning algorithms employed in the present study predicted lumpy disease with accuracy and F1 score of 100% and 1.0, respectively. In the present study, four different machine learning algorithms: Adaboost, K-nearest neighbors, decision tree and random forest are employed. The present research suggests that the decision trees can be used to predict lumpy skin disease infection using the geospatial and climatic parameters. The predicting power of machine learning algorithms can help in monitoring the disease spread patterns. It will also help in the application of vaccine campaigns in regions where the spread of disease poses a great risk to health.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169243
Akshay Kumar, Nitish Pathak, Madhu Kirola, Neelam Sharma, B. Rajakumar, K. Joshi
The computer mouse is one of the incredible inventions of Human-Computer Interaction. Wireless or Bluetooth mice we use currently are not free devices as they require batteries and dongles to plug into the Computer. Since computer vision is at its pinnacle and is used in many different aspects of day-to-day life, such as Face Recognition, Automatic car, and Color detection, we here are using it, to create an AI mouse by using hand tip detection and hand gestures. We also add face recognition using the Eigen face algorithm to revamp its security. The algorithm will first confirm the user’s authenticity by scanning their face once confirmed then one can access his computer through hand gestures, one can perform click and scroll the mouse without using the hardware mouse. The algorithm uses Eigenface and deep learning for the detection of hands.
{"title":"AI based mouse using Face Recognition and Hand Gesture Recognition","authors":"Akshay Kumar, Nitish Pathak, Madhu Kirola, Neelam Sharma, B. Rajakumar, K. Joshi","doi":"10.1109/ICAIA57370.2023.10169243","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169243","url":null,"abstract":"The computer mouse is one of the incredible inventions of Human-Computer Interaction. Wireless or Bluetooth mice we use currently are not free devices as they require batteries and dongles to plug into the Computer. Since computer vision is at its pinnacle and is used in many different aspects of day-to-day life, such as Face Recognition, Automatic car, and Color detection, we here are using it, to create an AI mouse by using hand tip detection and hand gestures. We also add face recognition using the Eigen face algorithm to revamp its security. The algorithm will first confirm the user’s authenticity by scanning their face once confirmed then one can access his computer through hand gestures, one can perform click and scroll the mouse without using the hardware mouse. The algorithm uses Eigenface and deep learning for the detection of hands.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130338772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A significant portion of the population suffers from various lung function disorders on a daily basis, which ultimately result in respiratory problems. For respiratory disorders to be managed effectively, prevention and early identification are crucial. Lung sound analysis has attracted more attention recently. So it’s likely that this discipline might one day allow for the automated inference of irregularities prior to respiratory collapse. An effective predictive model is required to reduce fatalities. The paper contrasts several feature extraction techniques applied in respiratory disorder classification models and offers an integrated solution for the issue. In this work, lung auscultation recordings are used to train a two-dimensional convolutional neural network (CNN) to identify respiratory diseases. In comparison to other models, the integrated solution significantly reduced the loss and attained an accuracy of 94.90%.
{"title":"Respiratory disorder classification based on lung auscultation using MFCC, Mel Spectrogram and Chroma STFT","authors":"Aditya Bapa, Omkar Bandgar, Arnav Ekapure, Jignesh Sisodia","doi":"10.1109/ICAIA57370.2023.10169299","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169299","url":null,"abstract":"A significant portion of the population suffers from various lung function disorders on a daily basis, which ultimately result in respiratory problems. For respiratory disorders to be managed effectively, prevention and early identification are crucial. Lung sound analysis has attracted more attention recently. So it’s likely that this discipline might one day allow for the automated inference of irregularities prior to respiratory collapse. An effective predictive model is required to reduce fatalities. The paper contrasts several feature extraction techniques applied in respiratory disorder classification models and offers an integrated solution for the issue. In this work, lung auscultation recordings are used to train a two-dimensional convolutional neural network (CNN) to identify respiratory diseases. In comparison to other models, the integrated solution significantly reduced the loss and attained an accuracy of 94.90%.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123466284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169530
P. Sankar, R. Vallikannu, G. Justin, Steve Karg
The lighting technology used for residential and commercial establishments has improved tremendously in recent years. From the era of incandescent lamps to the modern LED lighting systems, the transition is remarkable with power saving, better illumination, mood lighting thereby improving the user satisfaction. In this paper a localized strategy for LED lighting control is proposed, whereby available illumination is utilized judiciously. Additional integration of the control with the atmosphere in the room, no of persons occupying the room and matching the illumination with music is also considered.
{"title":"Ambient Intelligence based LED Lighting Control System Using BACnet Protocol","authors":"P. Sankar, R. Vallikannu, G. Justin, Steve Karg","doi":"10.1109/ICAIA57370.2023.10169530","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169530","url":null,"abstract":"The lighting technology used for residential and commercial establishments has improved tremendously in recent years. From the era of incandescent lamps to the modern LED lighting systems, the transition is remarkable with power saving, better illumination, mood lighting thereby improving the user satisfaction. In this paper a localized strategy for LED lighting control is proposed, whereby available illumination is utilized judiciously. Additional integration of the control with the atmosphere in the room, no of persons occupying the room and matching the illumination with music is also considered.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122284016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169499
D. Rout, Archana Kotangale, Sayantan Nath, Bholanath Roy
In this article, an association-based approach is proposed for determining the feature importance of a given dataset which includes the target variable. In particular, the concept of Market Basket Analysis (MBA) is applied for enumerating the relationships between the target variable and each of the features which lead to the importance of those. Mention that the MBA is generally used for obtaining the recommended items based on the togetherness of the items. Nevertheless, an attempt is made in this paper to correlate the features given a target output by abstracting each feature to be paired with the target variable. The apriori algorithm and association rules are used for accounting for the coupling of features with the target feature. Precisely, Lift metric of MBA is the key to computing the associativity in this context. That is, each feature’s importance is the sum of the individual ratio of Lift count of its values (observations) when paired with the target feature. The proposed methodology is tested on a dataset of a garment retail store that has information on several dresses. Each dress contains fifteen features including sales which is the lone numerical feature amidst the categorical features. Note that the sales are influenced by some of the features which generally the customers look for to prefer a particular dress over others. The results of the proposed methodology suggest that a couple of features are instigating sales at a higher rate than others. The outcome of the developed methodology is able to define a clear grouping of features according to the importance related to the target variable. The proposed methodology is applicable to a dataset where the feature selection is with respect to a target feature which is generally done in the case of supervised learning.
{"title":"An Association Based Approach to Elicit and Measure Impact of Features on Sales of a Garment Retail","authors":"D. Rout, Archana Kotangale, Sayantan Nath, Bholanath Roy","doi":"10.1109/ICAIA57370.2023.10169499","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169499","url":null,"abstract":"In this article, an association-based approach is proposed for determining the feature importance of a given dataset which includes the target variable. In particular, the concept of Market Basket Analysis (MBA) is applied for enumerating the relationships between the target variable and each of the features which lead to the importance of those. Mention that the MBA is generally used for obtaining the recommended items based on the togetherness of the items. Nevertheless, an attempt is made in this paper to correlate the features given a target output by abstracting each feature to be paired with the target variable. The apriori algorithm and association rules are used for accounting for the coupling of features with the target feature. Precisely, Lift metric of MBA is the key to computing the associativity in this context. That is, each feature’s importance is the sum of the individual ratio of Lift count of its values (observations) when paired with the target feature. The proposed methodology is tested on a dataset of a garment retail store that has information on several dresses. Each dress contains fifteen features including sales which is the lone numerical feature amidst the categorical features. Note that the sales are influenced by some of the features which generally the customers look for to prefer a particular dress over others. The results of the proposed methodology suggest that a couple of features are instigating sales at a higher rate than others. The outcome of the developed methodology is able to define a clear grouping of features according to the importance related to the target variable. The proposed methodology is applicable to a dataset where the feature selection is with respect to a target feature which is generally done in the case of supervised learning.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132558055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Credit card use has become necessary due to the rapid growth of e-commerce and the Internet. Because of the growing use of credit cards, the number of scams related to them has also grown. Such issues may be addressed through data science, which, when combined with machine learning, cannot be underestimated. This goal, “Credit Card Fraud Detection,” aims to uncover the structure of a data set using ML (machine learning). There are a variety of strategies that may be used to identify fraudulent activities. The primary objectives of this approach are to achieve the highest possible degree of precision, a high rate of successfully detecting fraudulent activity, and a low number of false positives. Customer behaviors have been included in this proposed work to identify fraudulent activities. The Random Forest Algorithm has the highest accuracy and MCC scores of all the algorithms. It has been found that the random forest algorithm has the greatest accuracy (94.4 percent) in detecting fraudulent credit card activity. Kaggle provided the dataset that was used in the analysis of credit card fraud
{"title":"Customer behavior-based fraud detection of credit card using a random forest algorithm","authors":"Narendra Kumar, Kunal Tomar, Tushar Sharma, Piyush Jyala, Dhruv Malik, Ishaan Dawar","doi":"10.1109/ICAIA57370.2023.10169484","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169484","url":null,"abstract":"Credit card use has become necessary due to the rapid growth of e-commerce and the Internet. Because of the growing use of credit cards, the number of scams related to them has also grown. Such issues may be addressed through data science, which, when combined with machine learning, cannot be underestimated. This goal, “Credit Card Fraud Detection,” aims to uncover the structure of a data set using ML (machine learning). There are a variety of strategies that may be used to identify fraudulent activities. The primary objectives of this approach are to achieve the highest possible degree of precision, a high rate of successfully detecting fraudulent activity, and a low number of false positives. Customer behaviors have been included in this proposed work to identify fraudulent activities. The Random Forest Algorithm has the highest accuracy and MCC scores of all the algorithms. It has been found that the random forest algorithm has the greatest accuracy (94.4 percent) in detecting fraudulent credit card activity. Kaggle provided the dataset that was used in the analysis of credit card fraud","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130055637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169687
A. Vinora, E. Lloyds, R. Nancy Deborah, M.S. Anandha Surya, V. Krithik Deivarajan, M. MuthuVignesh
Heart Disease is one of the prominent fatal diseases that have caused a colossal amount of deaths over decades. Machine learning an effective domain has been a key factor to solve various problems over a wide spread of areas. If the presence or the indication of such a fatal disease can be predicted in advance, it will be effortless for doctors to diagnose them. The ensemble stacked model which offers a way to combine Support Vector Machine (SVM) and Decision Tree(DT) models is part of the Machine learning domain that has been applied in our model to develop an intelligent system to predict the accuracy of the disease. The ensemble model of SVM and DT has achieved a higher percentage of efficiency among the various methods used for prediction. The proposed system presents a machine-learning approach for predicting heart disease, using a dataset of significant health factors such as age, sex, cholesterol, blood pressure, and sugar, from patients. The proposed system enables precise prediction of heart disease that enhances medical care and reduces the cost incurred for prediction. The dataset has been obtained from Kaggle.
{"title":"Heart Disease Prediction using Ensemble Model","authors":"A. Vinora, E. Lloyds, R. Nancy Deborah, M.S. Anandha Surya, V. Krithik Deivarajan, M. MuthuVignesh","doi":"10.1109/ICAIA57370.2023.10169687","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169687","url":null,"abstract":"Heart Disease is one of the prominent fatal diseases that have caused a colossal amount of deaths over decades. Machine learning an effective domain has been a key factor to solve various problems over a wide spread of areas. If the presence or the indication of such a fatal disease can be predicted in advance, it will be effortless for doctors to diagnose them. The ensemble stacked model which offers a way to combine Support Vector Machine (SVM) and Decision Tree(DT) models is part of the Machine learning domain that has been applied in our model to develop an intelligent system to predict the accuracy of the disease. The ensemble model of SVM and DT has achieved a higher percentage of efficiency among the various methods used for prediction. The proposed system presents a machine-learning approach for predicting heart disease, using a dataset of significant health factors such as age, sex, cholesterol, blood pressure, and sugar, from patients. The proposed system enables precise prediction of heart disease that enhances medical care and reduces the cost incurred for prediction. The dataset has been obtained from Kaggle.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134354744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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